The present invention relates to training a landmark detector using training data with noisy annotations.
In object detection, such as landmark detection, one of the biggest obstacles is obtaining accurate annotations. A landmark is defined as a distinct and unique anatomical structure useful for other tasks, such as image registration. Some anatomical structures that constitute landmarks include the lung apex, liver top, and tracheal bifurcation. Obtaining annotations can often be a very tedious and/or error prone task. Typically, to facilitate accurate landmark detection, a large amount of accurately annotated training data is needed. Training of landmark detectors is important in accurately finding the positions of anatomical landmarks in medical images and medical volumes.
To properly train a landmark detector to accurately detect landmarks, accurate annotations of landmark locations within training data is necessary. For example, a domain expert may provide accurate annotations by accurately indicating ground truth positions within training data images and volumes. On the other hand, novice annotators may provide noisy annotations that may be within a tolerable distance from ground truth positions. Thus, during training of landmark detectors using a mixture of annotated training data from both experts and novice annotators, there may be a mixture of both accurate annotations providing positive training samples, and noisy annotations that only provide a rough indication of where the positive training samples are actually located.